Automated Document Cluster Merging for Topic-Based Digital Assistant Interpretation

摘要

Disclosed are techniques for automatically extracting discovered topics and/or from determined discourse clusters for the generation of a language model that is applicable to interpreting commands received from a digital assistant device. An electronic document corpus can be generated having a plurality of documents that are clustered based on entropy, among other things. The clusters can be associated with a corresponding plurality of cluster attractors that are generally representative of a context of the documents included therein. The documents within the cluster for each of the document clusters can be analyzed, so that clusters determined representative of a hierarchical discourse community can be determined and logically merged. The merged clusters can be analyzed, such that topics and/or sub-topics can be determined and extracted therefrom, for indexing and storage, among other things. In this way, a more efficient searching of the electronic document corpus to interpret received inputs, such as commands received via a digital assistant device, can be facilitated.